Percentage of passed SQL condition on table

Table level check that ensures that a minimum percentage of rows passed a custom SQL condition (expression).

PROBLEM

America’s Health Rankings provides an analysis of national health on a state-by-state basis by evaluating a historical and comprehensive set of health, environmental and socioeconomic data to determine national health benchmarks and state rankings.

The platform analyzes more than 340 measures of behaviors, social and economic factors, physical environment and clinical care data. Data is based on public-use data sets, such as the U.S. Census and the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS), the world’s largest, annual population-based telephone survey of over 400,000 people.

Data structure

The following is a fragment of the bigquery-public-data.america_health_rankings.ahr dataset. Some columns were omitted for clarity.

value	lower_ci	upper_ci	source	source_date
87	87	87	U.S. Census Bureau, American Community Survey PUMS	2015-2019
87	87	87	U.S. Census Bureau, American Community Survey PUMS	2015-2019
87	86	87	U.S. Census Bureau, American Community Survey PUMS	2015-2019
79	79	79	U.S. Census Bureau, American Community Survey PUMS	2015-2019
87	86	87	U.S. Census Bureau, American Community Survey PUMS	2015-2019
87	87	88	U.S. Census Bureau, American Community Survey PUMS	2015-2019
88	88	88	U.S. Census Bureau, American Community Survey PUMS	2015-2019
77	76	77	U.S. Census Bureau, American Community Survey PUMS	2015-2019
78	78	79	U.S. Census Bureau, American Community Survey PUMS	2015-2019

We want to verify that the upper_ci column is always greater or equal to the lower_ci column. The check result should tell us if we have any rows not matching an SQL expression upper_ci >=lower_ci evaluated on each row.

The SQL query that can calculate the percentage of rows that passed the check should be like:

SELECT
     SUM(
          CASE
              WHEN 
                   upper_ci >= lower_ci
              THEN 1
              ELSE 0
          END) * 100.0 / COUNT(*)  AS actual_value
FROM `bigquery-public-data`.`america_health_rankings`.`ahr` AS analyzed_table

We want to verify that the percent of rows passed a custom SQL condition (expression) matches the expected threshold, for example at least 95% passed the data quality check or a data quality issue is raised.

SOLUTION

We will verify the data of bigquery-public-data.america_health_rankings.ahr using profiling sql_condition_passed_percent table check. Our goal is to verify if the percentage of rows passed a custom SQL condition does not fall below the setup thresholds.

In this example, we will set three minimum percentage thresholds levels for the check:

• warning: 100.0%
• error: 99.0%
• fatal: 95.0%

If you want to learn more about checks and threshold levels, please refer to the DQO concept section.

VALUE

If the percentage of data falls below 100.0%, a warning alert will be triggered.

YAML configuration file

The YAML configuration file stores both the table details and checks configurations.

In this example, we have set three minimum percentage thresholds levels for the check:

• warning: 100.0%
• error: 99.0%
• fatal: 95.0%

The highlighted fragments in the YAML file below represent the segment where the profiling sql_condition_passed_percent check is configured.

If you want to learn more about checks and threshold levels, please refer to the DQO concept section.

The data quality check must be configured as presented in the following YAML file:

apiVersion: dqo/v1
kind: table
spec:
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  profiling_checks:
    sql:
      profile_sql_condition_passed_percent_on_table:
        parameters:
          sql_condition: upper_ci >= lower_ci
        warning:
          min_percent: 100.0
        error:
          min_percent: 99.0
        fatal:
          min_percent: 95.0
  columns:
    edition:
      type_snapshot:
        column_type: INT64
        nullable: true

Running the checks in the example and evaluating the results using the graphical interface

The detailed explanation of how to run the example is described here.

To execute the check prepared in the example using the graphical interface:

1. Go to Profiling section.

2. Select the table or column mentioned in the example description from the tree view on the left.

3. Select Advanced Profiling tab.

4. Run the enabled check using the Run check button.

5. Review the results by opening the Check details button.

6. You should see the results as the one below. The actual value in this example is 92, which is below the minimum threshold level set in the warning (100.0%). The check gives a fatal error (notice the red square on the left of the name of the check).

1. After executing the checks, synchronize the results with your DQO cloud account sing the Synchronize button located in the upper right corner of the graphical interface.

2. To review the results on the data quality dashboards go to the Data Quality Dashboards section and select the dashboard from the tree view on the left. Below you can see the results displayed on the Affected tables per KPI dashboard showing results by issues per connection, issues per schema, issues per data quality dimension and issues per check category.

Running the checks in the example and evaluating the results using DQO Shell

The detailed explanation of how to run the example is described here.

To execute the check prepared in the example, run the following command in DQO Shell:

check run

You should see the results as the one below.

Check evaluation summary per table:
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
|Connection             |Table                      |Checks|Sensor results|Valid results|Warnings|Errors|Fatal errors|Execution errors|
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
|america_health_rankings|america_health_rankings.ahr|1     |1             |0            |0       |0     |**1**       |0               |
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+

As you can see, the table failed the data quality check raising a fatal severity error. The percent of passed SQL expressions is below the 95.0% and the check raised the Fatal error.

For a more detailed insight of how the check is run, you can initiate the check in debug mode by executing the following command:

check run --mode=debug

In the debug mode you can view the SQL query (sensor) executed in the check.

**************************************************
Executing SQL on connection america_health_rankings (bigquery)
SQL to be executed on the connection:
SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
                         CASE
                             WHEN (upper_ci >= lower_ci)
                                  THEN 1
                             ELSE 0
                         END) / COUNT(*)
    END AS actual_value,
    CURRENT_TIMESTAMP() AS time_period,
    TIMESTAMP(CURRENT_TIMESTAMP()) AS time_period_utc
FROM `bigquery-public-data`.`america_health_rankings`.`ahr` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
**************************************************

You can also see the results returned by the sensor. Now the actual value in this example is 92.6025888185073%, which is below the minimum threshold level set in the Fatal error (95.0%).

**************************************************
Finished executing a sensor for a check sql_condition_passed_percent_on_table on the table america_health_rankings.ahr using a sensor definition table/sql/sql_condition_passed_percent, sensor result count: 1

Results returned by the sensor:
+----------------+------------------------+------------------------+
|actual_value    |time_period             |time_period_utc         |
+----------------+------------------------+------------------------+
|92.6025888185073|2023-05-18T08:29:25.667Z|2023-05-18T08:29:25.667Z|
+----------------+------------------------+------------------------+
**************************************************

The expression upper_ci >=lower_ci was false for almost 7.4% rows probably because the column upper_ci or lower_ci is NULL so the expression was false.

Let's update the SQL expression and count rows with NULL values as valid.

apiVersion: dqo/v1
kind: table
spec:
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  profiling_checks:
    sql:
      profile_sql_condition_passed_percent_on_table:
        parameters:
          sql_condition: upper_ci >= lower_ci or upper_ci is NULL or lower_ci is NULL
        warning:
          min_percent: 100.0
        error:
          min_percent: 99.0
        fatal:
          min_percent: 95.0
  columns:
    edition:
      type_snapshot:
        column_type: INT64
        nullable: true

Running the checks in the example and evaluating the results using the graphical interface

The detailed explanation of how to run the example is described here.

To execute the check prepared in the example using the graphical interface:

1. Go to Profiling section.

2. Select the table or column mentioned in the example description from the tree view on the left.

3. Select Advanced Profiling tab.

4. Run the enabled check using the Run check button.

5. Review the results by opening the Check details button.

6. You should see the results as the one below. The actual value in this example is 100. The check gives a valid result (notice the green square on the left of the name of the check).

1. After executing the checks, synchronize the results with your DQO cloud account sing the Synchronize button located in the upper right corner of the graphical interface.

2. To review the results on the data quality dashboards go to the Data Quality Dashboards section and select the dashboard from the tree view on the left. Below you can see the results displayed on the Total DQ KPIs per category dashboard showing results by KPI, KPI per check category, Datetime category, Pii KPI and Standard KPI.

Running the checks in the example and evaluating the results using DQO Shell

The detailed explanation of how to run the example is described here.

To execute the check prepared in the example, run the following command in DQO Shell:

check run

You should see the results as the one below. The percent of passed SQL expressions is 100.0% so the result is valid.

Check evaluation summary per table:
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
|Connection             |Table                      |Checks|Sensor results|Valid results|Warnings|Errors|Fatal errors|Execution errors|
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
|america_health_rankings|america_health_rankings.ahr|1     |1             |1            |0       |0     |0           |0               |
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+

For a more detailed insight of how the check is run, you can initiate the check in debug mode by executing the following command:

check run --mode=debug

In the debug mode you can view the SQL query (sensor) executed in the check.

**************************************************
Executing SQL on connection america_health_rankings (bigquery)
SQL to be executed on the connection:
SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
                         CASE
                             WHEN (upper_ci >= lower_ci or upper_ci is NULL or lower_ci is NULL)
                                  THEN 1
                             ELSE 0
                         END) / COUNT(*)
    END AS actual_value,
    CURRENT_TIMESTAMP() AS time_period,
    TIMESTAMP(CURRENT_TIMESTAMP()) AS time_period_utc
FROM `bigquery-public-data`.`america_health_rankings`.`ahr` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
**************************************************

You can also see the results returned by the sensor. The actual value in this example is 100.0%.

**************************************************
Finished executing a sensor for a check sql_condition_passed_percent_on_table on the table america_health_rankings.ahr using a sensor definition table/sql/sql_condition_passed_percent, sensor result count: 1

Results returned by the sensor:
+------------+------------------------+------------------------+
|actual_value|time_period             |time_period_utc         |
+------------+------------------------+------------------------+
|100.0       |2023-05-18T08:38:22.311Z|2023-05-18T08:38:22.311Z|
+------------+------------------------+------------------------+
**************************************************